Decentralized computing networks, architectures and techniques for processing events across multiple channels

ABSTRACT

This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.

TECHNICAL FIELD

This disclosure is related to decentralized computing networks,architectures, and techniques for processing events across multiplechannels corresponding to distinct geographic regions, as well assystems, methods, and techniques for the same.

BACKGROUND

Traditional systems for monitoring or analyzing data across largegeographic areas typically utilize a centralized network configuration.With a centralized network configuration, all data is collected foranalysis and processing by a single, central authority (e.g., a centralserver) that manages data storage, communications, and data processingoperations across the network. For example, in a typical scenario, acentral authority may aggregate data across large geographic areas,separately analyze the data for each of the geographic areas, andcommunicate updates or instructions to devices across the network.Hence, all analysis and decision making across the geographic areas isperformed by the central authority.

Utilizing a centralized network configuration to collect, analyze, andprocess data across large geographic areas can be disadvantageous for avariety of reasons. One disadvantage relates to that fact thatprocessing aggregated data across large geographic areas with a singlecentral authority can be computationally expensive, and can requirelarge allocations of network storage and processing resources. Moreover,processing large collections of data in this manner can result innetwork latency, which can be attributed, at least in part, to the timerequired to process the data and communicate with devices over thenetwork. In scenarios in which systems are intended to provide real-timedata or functionality, these latency issues can significantly affectperformance of the system and can prevent updates or other data frombeing disseminated across the network in a timely fashion.

Another disadvantage of traditional, centralized systems relates totheir lack of scalability and flexibility. Centralized systems can bedifficult to scale past a certain point because doing so typicallyrequires additional storage, bandwidth, and/or processing power to beprovided to the central authority. This can be particularly problematicin scenarios in which a system is rapidly expanding to cover newgeographic areas, or in scenarios in which data-intensive upgrades aredesired to improve the functionality of the system in existinggeographic areas.

A further disadvantage of centralized systems can be attributed to thefact that the central authority serves as a single point of failure. Ifthe central authority fails (either for technical reasons or duemalicious cyberattacks), the entire system or network also will fail andbecome unavailable.

BRIEF DESCRIPTION OF DRAWINGS

To facilitate further description of the embodiments, the followingdrawings are provided, in which like references are intended to refer tolike or corresponding parts, and in which:

FIG. 1A is a diagram of an exemplary system according to certainembodiments;

FIG. 1B is a block diagram illustrating exemplary features of surgeanalytics platform according to certain embodiments;

FIG. 2A is a diagram illustrating an exemplary geographical hierarchy ofnodes for a decentralized computing network according to certainembodiments;

FIG. 2B is a block diagram illustrating a cloud-based computingenvironment for a decentralized computing network according to certainembodiments;

FIG. 2C is a diagram illustrating a decentralized computing network thatis configured to execute a demand adjustment function according tocertain embodiments;

FIG. 2D is a diagram illustrating a decentralized computing network thatis configured to execute a demand adjustment function according tocertain embodiments;

FIG. 3 is an illustration demonstrating how a decentralized computingnetwork can generate channel analysis data across various verticalsaccording to certain embodiments;

FIG. 4 is an illustration demonstrating aspects of a predictive engineaccording to certain embodiments;

FIG. 5A is a block diagram illustrating exemplary channel eventsaccording to certain embodiments;

FIG. 5B is a block diagram illustrating exemplary channel analysis dataaccording to certain embodiments;

FIG. 6 is a block diagram of an exemplary client system according tocertain embodiments; and

FIG. 7 is a flowchart illustrating an exemplary method according tocertain embodiments.

The terms “first,” “second,” “third,” “fourth,” and the like in thedescription and in the claims, if any, are used for distinguishingbetween similar elements and not necessarily for describing a particularsequential or chronological order. It is to be understood that the termsso used are interchangeable under appropriate circumstances such thatthe embodiments described herein are, for example, capable of operationin sequences other than those illustrated or otherwise described herein.

The terms “upper,” “lower,” “left,” “right,” “front,” “rear,” “back,”“top,” “bottom,” “over,” “under,” and the like in the description and inthe claims, if any, are used for descriptive purposes and notnecessarily for describing permanent relative positions. It is to beunderstood that the terms so used are interchangeable under appropriatecircumstances such that the embodiments of the systems, methods, and/orarticles of manufacture described herein are, for example, capable ofoperation in other orientations than those illustrated or otherwisedescribed herein.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present disclosure relates to systems, methods, apparatuses, andcomputer program products that utilize decentralized computing networks,architectures and techniques to collect, analyze, and process data overmultiple geographic areas. In certain embodiments, a decentralizedcomputing network comprises a plurality of computing nodes, each ofwhich is configured to collect, analyze, and process channel events fora channel that corresponds to a particular geographic region. Each nodeof the decentralized computing network can operate independently toprocess data originating from, or associated with, a given channel. Forexample, each node can receive channel events originating from, orassociated with, a given channel, and can execute a channel analysisfunction to derive channel analysis data pertaining to the channel orits corresponding geographic region. As described in further detailbelow, the channel analysis data generated by each node can be used forvarious purposes. The decentralized configuration of the nodes enablesefficient processing of data collected over large geographic areas,increases the reliability of the system, and facilitates easy scaling ofthe system.

In certain embodiments, the nodes included in the decentralizedcomputing network can be arranged in a geographical hierarchy that isbased on the channels or geographic areas associated with the nodes. Thegeographical hierarchy can establish parent-child relationships amongthe nodes, such that each child node corresponds to a geographic regionthat is a subset of a geographic region associated with a parent node.While each node can operate independently to process data for acorresponding channel or geographic region, a node communicationprotocol can enable information to be exchanged among the nodes based onrelationships of the nodes to each other. For example, in some cases, agiven node can exchange information with one or more parent nodes and/orone or more child nodes based on geographical associations orrelationships. The exchange of information among the nodes avoidsredundancies, reduces storage requirements, and increases efficiencywith respect to generating the channel analysis data.

The types of channel events received and processed by each node canvary. The channel events can generally include any data relating toindividuals located within a channel, activities occurring within thechannel, and/or other conditions associated with the channel. Forexample, in some embodiments, the channel events received by a node fora given channel can include data indicating locations or movements ofindividuals (or devices operated by individuals) within the channel. Thechannel events also can include data relating to transactions conductedwithin the channel, weather conditions within the channel, merchantslocated within the channel, and events (e.g., concerts, conventions,sporting games, etc.) occurring with the channel. The channel events caninclude many additional attributes related to the channel itself and/orusers within the channel. Further examples of channel events aredescribed throughout this disclosure.

Each node of the decentralized computing network can execute a channelanalysis function that is configured to analyze or process the channelevents, and generate channel analysis data for a corresponding channelassociated with the node. The channel analysis data generated by eachnode can vary. The channel analysis data for a given channel can includevarious metrics relating to the channel itself, individuals within thechannel, and/or activities occurring within the channel. For example,the channel analysis data can indicate or predict the supply and/ordemand within the channel for one or more products or services (e.g.,riding sharing, hotel accommodations, parking garages, diningavailability, sporting or event tickets, etc.). The channel analysisdata also can identify or predict increases and/or decreases in thenumber of individuals within the channel, as well as intra-channel andinter-channel movements of those individuals. The channel analysis datacan include many other metrics related to the channel, individuals inthe channel, and/or activities occurring within the channel. Furtherexamples of channel analysis data are described throughout thisdisclosure.

The channel analysis data generated by each node can include real-timeanalysis information that provides current or up-to-date data relatingto the aforementioned metrics and/or other metrics. The channel analysisdata also can include predictive analysis information that predicts afuture status of the aforementioned metrics and/or other metrics.Examples of these real-time and predictive analytics are providedthroughout this disclosure.

In certain embodiments, each of the nodes can utilize the channelanalysis data to execute one or more demand adjustment functions.Additionally, or alternatively, the channel analysis data can beprovided to one or more client systems that utilize the channel analysisdata to execute the one or more demand adjustment functions. Oneexemplary demand adjustment function can include a pricing function thatdetermines prices for one or more inventory items. For example, in somecases, the channel analysis data can be utilized by a surge pricingfunction to dynamically adjust prices for one or more inventory items ina manner that accounts for the supply and demand for the one or moreinventory items. Another exemplary demand adjustment function includesan inventory management function that utilizes the channel analysis datato manage or adjust inventories (e.g., such as to dynamically reallocateinventory items among channels and/or initiate ordering of additionalinventory items).

The channel analysis data generated by a given node can be utilized toexecute one or more deployment functions that enable the channelanalysis data to be utilized for a variety of purposes. In somescenarios, a deployment function can be executed to automate pricingsystems (e.g., surge pricing systems) and/or inventory systems based onthe channel analysis data. For example, the channel analysis data can beinterfaced with pricing and inventory applications operated by one ormore client systems (e.g., third-party systems) to automatically adjustthe pricing of inventory (e.g., products or services) and/orautomatically reallocate or reorder inventory based on the supply anddemand metrics associated with the channel and/or based on the increasesor decreases of individuals within the channel.

In another example, a deployment function can be executed to transmitnotifications that include some or all of the channel analysis data. Forexample, notifications can be transmitted to client systems affiliatedwith merchants within the channel to disseminate real-time data and/orpredictive data relating to the supply and demand of particular productsor services. The merchant notifications also can include otherinformation included in the channel analysis data (e.g., populationsurge information, event information, transaction patterns within thechannel, movement patterns of individuals with the channel, etc.).Notifications also can be transmitted to individuals within the channel(e.g., to present offers or discounts to the individuals and/orrecommend merchants located in the channel). Additional types ofdeployment functions are described throughout this disclosure.

The technologies described herein provide a variety of benefits andadvantages. Amongst other things, the decentralized computing techniquesenable rapid and efficient processing of data collected over multiplegeographic areas. This can be attributed, at least in part, to thedecentralization of nodes that act independently to collect and processinformation for specific channels, rather than relying on a centralauthority to aggregate and analyze the data across multiple geographicareas. Another benefit of the centralized computing techniques relatesto increases the reliability of the system. In the event that one of thenodes fail, the other nodes can continue to function independently.Additionally, the failed node easily can be replicated and reinitializedto minimize downtime. Another benefit relates to the ease of scaling ofthe system, such as to accommodate new geographic areas and/or enhancedfunctionality. When geographic scaling is desired, additional nodes caneasily be incorporated into the geographical hierarchy without having tomodify the functionality of a central authority. Likewise, when enhancedfunctionality is desired, additional computing and storage resources caneasily be allocated to existing nodes.

Further benefits can be attributed to the usefulness and versatility ofthe channel analysis data, which can be leveraged for many differentpurposes. This data can provide merchant entities with real-time and/orpredictive metrics that can assist merchant entities with accommodatingsurges in supply and demand (and/or population surges within a channel).In some cases, the channel analysis data also can be integrated withpricing and/or inventory systems to seamlessly adjust pricing and/orinventory allocations in real-time or near real-time.

Additional benefits can be attributed to embodiments that utilize thechannel analysis data to automated surge pricing functions. Clientapplications that employ surge pricing functionalities can bettermitigate imbalances between an available supply of inventory items and ademand for those inventory items. The channel analysis can be leveragedto dynamically adjust prices for the inventory items, thereby enablingproviders of the client applications to reduce high-demand peaks.

The technologies discussed herein can be used in a variety of differentcontexts and environments. Some useful applications of thesetechnologies are in the context of adjusting pricing and/or inventoryallocations for merchant entities, such as those that provide ridehailing services, hotel accommodations, parking garages, bar andrestaurant establishments, etc.. For example, the technologies disclosedherein can provide merchant-specific insights and metrics regarding thecurrent and future demand for products and services offered by theseentities. These metrics can be used by those entities to automaticallyor manually adjust settings for pricing systems (e.g., surge pricingsystems), inventory systems, and/or other operations.

The embodiments described in this disclosure can be combined in variousways. Any aspect or feature that is described for one embodiment can beincorporated to any other embodiment mentioned in this disclosure.Moreover, any of the embodiments described herein may be hardware-based,may be software-based, or, preferably, may comprise a mixture of bothhardware and software elements. Thus, while the description herein maydescribe certain embodiments, features, or components as beingimplemented in software or hardware, it should be recognized that anyembodiment, feature and/or component referenced in this disclosure canbe implemented in hardware and/or software.

FIG. 1A is a diagram of an exemplary system 100 in accordance withcertain embodiments. The system 100 includes, inter alia, an analyticsplatform 150 that utilizes a decentralized computing network 160 togenerate or derive channel analysis data 180 for a plurality of channels170. FIG. 1B is a diagram illustrating additional features, components,and/or functions associated with the analytics platform 150 anddecentralized computing network 160. FIGS. 1A and 1B are jointlydiscussed below.

The system 100 comprises one or more computing devices 110, one or moreservers 120, one or more external data sources 130, and one or moreclient systems 140 that are in communication over a network 105. Ananalytics platform 150 comprising a decentralized computing network 160is stored on, and executed by, the one or more servers 120. The network105 may represent any type of communication network, e.g., such as onethat comprises a local area network (e.g., a Wi-Fi network), a personalarea network (e.g., a Bluetooth network), a wide area network, anintranet, the Internet, a cellular network, a television network, and/orother types of networks.

All the components illustrated in FIG. 1A, including the one or morecomputing devices 110, one or more servers 120, one or more externaldata sources 130, and one or more client systems 140, and analyticsplatform 150 can be configured to communicate directly with each otherand/or over the network 105 via wired or wireless communication links,or a combination of the two. Each of the computing devices 110, servers120, external data sources 130, client systems 140, and analyticsplatform 150 can include one or more communication devices, one or morecomputer storage devices 101, and one or more processing devices 102(FIG. 1B) that are capable of executing computer program instructions.

The one or more computer storage devices 101 may include (i)non-volatile memory, such as, for example, read only memory (ROM) and/or(ii) volatile memory, such as, for example, random access memory (RAM).The non-volatile memory may be removable and/or non-removablenon-volatile memory. Meanwhile, RAM may include dynamic RAM (DRAM),static RAM (SRAM), etc. Further, ROM may include mask-programmed ROM,programmable ROM (PROM), one-time programmable ROM (OTP), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable ROM (EEPROM) (e.g., electrically alterable ROM (EAROM)and/or flash memory), etc. In certain embodiments, the one or morecomputing storage devices 101 may be physical, non-transitory mediums.The one or more computer storage devices 101 can store, inter alia,instructions associated the implementing the functionalities of theanalytics platform 150 and decentralized computing network 160 describedherein.

The one or more processing devices 102 may include one or more centralprocessing units (CPUs), one or more microprocessors, one or moremicrocontrollers, one or more controllers, one or more complexinstruction set computing (CISC) microprocessors, one or more reducedinstruction set computing (RISC) microprocessors, one or more very longinstruction word (VLIW) microprocessors, one or more graphics processorunits (GPU), one or more digital signal processors, one or moreapplication specific integrated circuits (ASICs), and/or any other typeof processor or processing circuit capable of performing desiredfunctions. The one or more processing devices 102 can be configured toexecute any computer program instructions that are stored or included onthe one or more computer storage devices 101 including, but not limitedto, instructions associated the implementing the functionalities of theanalytics platform 150 and decentralized computing network 160 describedthroughout this disclosure.

Each of the one or more communication devices can include wired andwireless communication devices and/or interfaces that enablecommunications using wired and/or wireless communication techniques.Wired and/or wireless communication can be implemented using any one orcombination of wired and/or wireless communication network topologies(e.g., ring, line, tree, bus, mesh, star, daisy chain, hybrid, etc.)and/or protocols (e.g., personal area network (PAN) protocol(s), localarea network (LAN) protocol(s), wide area network (WAN) protocol(s),cellular network protocol(s), powerline network protocol(s), etc.).Exemplary PAN protocol(s) can comprise Bluetooth, Zigbee, WirelessUniversal Serial Bus (USB), Z-Wave, etc. Exemplary LAN and/or WANprotocol(s) can comprise Institute of Electrical and ElectronicEngineers (IEEE) 802.3 (also known as Ethernet), IEEE 802.11 (also knownas WiFi), etc. Exemplary wireless cellular network protocol(s) cancomprise Global System for Mobile Communications (GSM), General PacketRadio Service (GPRS), Code Division Multiple Access (CDMA),Evolution-Data Optimized (EV-DO), Enhanced Data Rates for GSM Evolution(EDGE), Universal Mobile Telecommunications System (UMTS), DigitalEnhanced Cordless Telecommunications (DECT), Digital AMPS (IS-136/TimeDivision Multiple Access (TDMA)), Integrated Digital Enhanced Network(iDEN), Evolved High-Speed Packet Access (HSPA+), Long-Term Evolution(LTE), WiMAX, etc. The specific communication software and/or hardwarecan depend on the network topologies and/or protocols implemented. Incertain embodiments, exemplary communication hardware can comprise wiredcommunication hardware including, but not limited to, one or more databuses, one or more universal serial buses (USBs), one or more networkingcables (e.g., one or more coaxial cables, optical fiber cables, twistedpair cables, and/or other cables). Further exemplary communicationhardware can comprise wireless communication hardware including, forexample, one or more radio transceivers, one or more infraredtransceivers, etc. Additional exemplary communication hardware cancomprise one or more networking components (e.g., modulator-demodulatorcomponents, gateway components, etc.). In certain embodiments, the oneor more communication devices can include one or more transceiverdevices, each of which includes a transmitter and a receiver forcommunicating wirelessly. The one or more communication devices also caninclude one or more wired ports (e.g., Ethernet ports, USB ports,auxiliary ports, etc.) and related cables and wires (e.g., Ethernetcables, USB cables, auxiliary wires, etc.).

In certain embodiments, the one or more communication devicesadditionally, or alternatively, can include one or more modem devices,one or more router devices, one or more access points, and/or one ormore mobile hot spots. For example, modem devices may enable some or allof the computing devices 110, servers 120, external data sources 130,client systems 140, and/or analytics platform 150 to be connected to theInternet and/or other network. The modem devices can permitbi-directional communication between the Internet (and/or other network)and the computing devices 110, servers 120, external data sources 130,client systems 140, and/or analytics platform 150. In certainembodiments, one or more router devices and/or access points may enablethe computing devices 110, servers 120, external data sources 130,client systems 140, and/or analytics platform 150 to be connected to aLAN and/or other more other networks. In certain embodiments, one ormore mobile hot spots may be configured to establish a LAN (e.g., aWi-Fi network) that is linked to another network (e.g., a cellularnetwork). The mobile hot spot may enable the computing devices 110,servers 120, external data sources 130, client systems 140, and/oranalytics platform 150 to access the Internet and/or other networks.

In certain embodiments, the computing devices 110 may represent desktopcomputers, laptop computers, mobile devices (e.g., smart phones,personal digital assistants, tablet devices, vehicular computingdevices, wearable devices, or any other device that is mobile innature), and/or other types of devices. The one or more servers 120 maygenerally represent any type of computing device, including any of theaforementioned computing devices 110. The one or more servers 120 alsocan comprise one or more mainframe computing devices and/or one or morevirtual servers that are executed in a cloud-computing environment. Insome embodiments, the one or more servers 120 can be configured toexecute web servers and can communicate with the computing devices 110,external data sources 130, client systems 140, and/or other devices overthe network 105 (e.g., over the Internet).

In certain embodiments, the analytics platform 150 can be stored on, andexecuted by, the one or more servers 120. Additionally, oralternatively, the analytics platform 150 can be stored on, and executedby, the one or more computing devices 110 and/or one or more clientsystems 140. The analytics platform 150 can be executed be stored on,and executed, by other devices as well.

In some embodiments, the analytics platform 150 also can be stored as alocal application on a computing device 110, or interfaced with a localapplication stored on a computing device 110, to implement thetechniques and functions described herein. The computing device 110 maybe part of client system 140 in some scenarios.

The client systems 140 can generally correspond to third-party systems,networks, and/or devices that access the analytics platform 150 and/orutilize the data (including the channel analysis data 180) generated bythe analytics platform 150. For example, the client systems 140 can beoperated and managed by individuals, businesses, and/or other entitiesthat utilize the analytics platform 150 (including the channel analysisdata 180 generated by the analytics platform 150) to improve thefunctionalities of one or more systems and/or one or more applications.

In certain embodiments, each of the client systems 150 can registerand/or create a user account with the analytics platform 150 to obtainaccess to the data and services provided by the analytics platform 150.The client systems 180 can be operated by, or associated with,individuals or businesses from any industry or vertical including, butnot limited, to those that offer ride hailing services, hotel or lodgingaccommodations, parking space availability, tavern services, andrestaurant services. As explained in further detail below, the clientsystems 180 can utilize the channel analysis data 180 (and other dataprovided by the analytics platform 150) to enhance and improve businessoperations in various ways.

Each of the client systems 140 may include one or more computing devices110 that enable the client systems 140 to access the analytics platform150 over the network 105. In some cases, one or more of the clientsystems 140 may include sophisticated technological infrastructures,such those that include enterprise systems, servers 120, virtual privatenetworks (VPNs), intranets, etc. The computing devices 110, servers 120,and/or other devices associated with each client system 140 can storeand execute various applications (e.g., such as ride hailingapplications, lodging booking applications, dining reservationapplications, pricing applications, inventory management applications,etc.). The client systems 140 and associated applications can leveragethe data (e.g., channel analysis data 180) provided by the analyticsplatform 150 in various ways.

In certain embodiments, the analytics platform 150 can be integratedwith (or can communicate with) various applications hosted by the clientsystems 140 including, but not limited to, applications that provideproducts or services for transportation services (e.g., ride hailingservices, ride sharing services, vehicle rental services, and/or ticketscheduling services for buses, trains, planes, boats, and/or other modesof transportation), lodging accommodations (e.g., booking services forhotels, motels, short-term home stays, rental services, propertypurchases, etc.), parking space services (e.g., booking services forparking garages, parking lots, etc.), and scheduling services (e.g.,reservation services for restaurants, bars, sporting events, concerts,ticketed events, etc.). In certain embodiments, the analytics platform150 additionally, or alternatively, can be integrated with (or cancommunicate with) e-commerce applications, pricing applications,inventory management applications, and/or other applications.

The aforementioned applications and/or other applications, each of whichmay be integrated or interfaced with the analytics platform 150, can bestored on one or more client systems 140 in some embodiments. Forexample, the aforementioned applications and/or other applications canstored be stored on one or more computing device 110 and/or one or moreservers 120 associated with one or more client systems 140.

As discussed throughout this disclosure, the analytics platform 150 cangenerally provide functions associated with analyzing conditionsassociated with activities, individuals, events, and/or like within aplurality of channels 170. This analysis can be used to generate channelanalysis data 180 corresponding to each of the channels 170. The channelanalysis data 180 for a given channel 170 can include various metricsand information useful for understanding and/or predicting theconditions within the channel 170. For example, the channel analysisdata 180 for a channel 170 can include information that indicates and/orpredicts population surges within the channel 170, movements ofindividuals within the channel, and/or supply and demand for inventory(e.g., products or services) within the channel 170. Additionally, asdescribed in further detail below, the analytics platform 150 canutilize the channel analysis data 180 to implement a surge pricingfunction and/or inventory management function on a variety of clientapplications.

Each channel 170 can represent, or correspond to, a specific geographicregion or area. The scope or region associated with each channel 170 canvary significantly. For example, macro-level channels 170 can correspondto large geographic areas covering entire continents, countries, and/orstates. Other more micro-level channels 170 can correspond to counties,cities, and/or towns. Additional channels 170 can correspond to specificregions, neighborhoods, areas, or the like within cities or towns.Regardless of geographic scope, a node 165 included in the decentralizedcomputing network 160 can be associated with each channel 170 to processdata associated with the channel 170 and generate channel analysis data180 for the channel 170.

The nodes 165 and/or analytics system 150 can store location definitiondata 171 that defines the geographic region associated with each channel170. For example, in certain embodiments, the location definition data171 can store global positioning system (GPS) coordinates for eachchannel 170 that precisely defines the geographic region associated withthe channel 170. Additionally, or alternatively, the location definitiondata 171 can include geo-fencing data that defines the geographic regionassociated with the channel 170. In some embodiments, the analyticsplatform 150 can provide access to one or more GUIs that enable users todefine the geographic regions associated with the channels 170.

The analytics platform 150 can include, or communicate with, adecentralized computing network 160 that is configured to generate someor all of the channel analysis data 180 for the channels 170. Thedecentralized computing network 160 can comprise a plurality of nodes165, each of which is configured to execute a channel analysis function166 that independently processes information associated with a dedicatedchannel 170 and generates corresponding channel analysis data 180 forthat channel 170.

Each node 175 in the decentralized computing network 160 can bededicated to a particular channel 170 and can receive channel events 175associated with the channel 170. These channel events 175 can beanalyzed and/or utilized by the channel analysis function 166 togenerate the channel analysis data 180 for the channel 170.

Each node 175 of the decentralized computing network 160 can utilizededicated processing and/or storage resources to process the channelevents 175 and generate the channel analysis data 180 for acorresponding channel 170. In certain embodiments, the decentralizedcomputing network 160 can be provided via a cloud-based computingenvironment that executes a separate virtual machine for each node 165.Alternatively, or additionally, one or more hardware servers (or othercomputing devices) can be dedicated to each node 165.

Because the nodes 165 can be configured to separately and independentlyprocess data associated each channel 170, each node 165 only processesthe data corresponding to its designated channel 170 and does not needto process the global data across all channels 170. This decentralizedconfiguration can enable rapid and efficient processing of datacollected over multiple geographic areas, and can significantly decreaselatencies across the network. The configuration can be particularlybeneficial in scenarios in which the analytics platform 150 iscollecting and analyzing information over large geographic areas.Additionally, the independent processing of data by the nodes 165provides more reliability and drastically reduces the chances of acatastrophic system failure in which the entirety of the systems becomesunavailable. Additional details and exemplary configurations ofdecentralized computing networks 160 are described below with referenceto FIGS. 2A and B.

The types and content of the channel events 175 received and processedby each node 165 can vary. The channel events 175 can generally includeany data associated with monitoring locations of individuals within achannel 170, activities occurring within the channel 170I, and/or otherconditions associated with the channel 170. For example, the channelevents 175 received by a node 165 can include data indicating locationsof individuals (or their smart phones or mobile devices) within thechannel 170, transactions conducted within the channel 170, weatherconditions within the channel, and/or events (e.g., concerts,conventions, etc.) occurring within the channel 170.

The channel events 175 can be generated by, or received from, variousdevices, systems, and/or sources. Some of the channel events 175 can begenerated by computing devices 110 (e.g., mobile devices, smart phones,wearable devices, etc.) operated by individuals within the channels 170.For example, these devices (or applications installed thereon) cangenerate channel events 175 indicating locations of the devices,transactions conducted using the devices, and/or other information.

Additional channel events 175 can be received one or more external datasources 130, which can include third-party websites, databases, and/orservers that provide information relating to the channels 170 and/orindividuals located within the channels 170. Exemplary external datasources 130 can include websites, databases, and/or servers associatedwith cellular device providers, weather outlets, news outlets, socialmedia sites, and/or the like. In some embodiments, these and otherexternal data sources 130 be used to derive or generate channel events175 relating to weather conditions within the channels 170, eventsoccurring with the channel, locations of individuals within thechannels, etc.

FIG. 5A is block diagram illustrating examples of channel events 175that can be received by each of the nodes 165. As shown, the channelevents 175 can include, inter alia, location data 501, transaction data502, user demographic data 503, merchant data 504, weather data 505, andevent data 506.

The location data 501 received by each node 165 can indicate the currentlocations and/or historical locations and movements of individuals (orcomputing devices 110 operated by individuals) located in a channel 170associated with the node 165. For example, the location data 501 mayinclude GPS coordinates indicating the current locations of individuals,and previous locations of those individuals. In some embodiments, thelocation data 501 can be received directly from computing devices 110operated by the individuals and/or an external data source 130, such asa cellular service provider.

The transaction data 502 received by each node 165 can indicatepurchases that are made within a channel 170 associated with the node165 and/or purchases made by individuals that are currently locatedwithin the channel 170. The transaction data 502 also may indicatetransaction patterns or profiles for each of the individuals located inthe channel 170 (e.g., indicating the types of products or servicesroutinely purchased by the individual and/or the types of businessesroutinely frequented by the individual). In some cases, the transaction502 data also may indicate the channel 170 where each transaction wasconducted. In some embodiments, the transaction data 502 can be receiveddirectly from computing devices 110 operated by the individuals and/oran external data source 130, such as a third-party merchant system,credit card service provider, digital payments provider, etc.

The demographic information 503 received by each node 165 can includevarious characteristics or attributes relating to individuals within acorresponding channel 170 associated with the node 165. For example, theuser demographic data 503 for each individual can indicate some or allof the following: age, sex, race, ethnicity, income, marital status,occupation, education level, interests, etc. In some embodiments, thedemographic information 503 can be received directly from computingdevices 110 operated by the individuals and/or an external data source130, such as a social media sites, marketing or advertising services,etc.

The merchant data 504 received by each node 165 can provide informationrelated to merchants (e.g., businesses, vendors, etc.) located in achannel 170 associated with the node 165. For example, the merchant data504 may identify the locations of the merchants, the vertical associatedwith the merchants, hours of operation, and products or services offeredby the merchants. In some embodiments, the merchant data 504 can bereceived directly from computing devices 110 operated by the merchantsand/or an external data source 130, such as a crowd-sourced businessreview applications, business information databases, etc.

The weather data 505 received by each node 165 can indicate the currentweather conditions and/or historical weather conditions in a channel 170associated with the node 165. The weather data 505 also may indicateforecasts of future weather conditions for the channel 170. In someembodiments, the weather data 505 can be received from one or moreexternal data sources 130, such as those that provide weatherforecasting services.

The event data 505 received by each node 165 can provide informationassociated with events (e.g., concerts, conventions, seminars, shows,etc.) in the channel 170. The event data 505 can include informationidentifying ongoing events, as well as previously held or upcomingevents. The event data 505 may include dates and times associated witheach of the events 505. In some embodiments, the event data 505 can bereceived directly from computing devices 110 operated by event providersand/or an external data source 130, such as a social media sites,community bulletin board sites, etc.

The categories identified in FIG. 5A are intended to provide examples ofcontent that may be included in channel events 175 received by each node165 of the decentralized computing network 160. However, it should berecognized that the channel events 175 received by each node 165 caninclude additional information or data related to the activities,individuals, entities, and/or conditions of a channel 170.

Returning to FIGS. 1A and 1B, the channel events 175 associated with achannel 170 can be received by a node 165 dedicated or assigned to thatchannel 170. The node 165 can execute a channel analysis function 166 toanalyze the channel events 175, and generate channel analysis data 180for the channel 170. The channel analysis data 180 can include varioustypes of metrics or data useful for understanding the conditionsassociated with the channel 170. In many scenarios, the channel analysisdata 180 can include real-time information regarding the current channelconditions and/or predictions related to future channel conditions.

FIG. 5B is a block diagram illustrating examples of channel analysisdata 180 that can be generated by each node 165. In some embodiments,the channel analysis function 166 executed by each node 165 may generatechannel analysis data 180 that includes population surge metrics 511,movement tracking metrics 512, and demand metrics 513.

The population surge metrics 511 generated by each node 165 can detectoccurrences or situations in which the number of individuals locatedwithin a channel 170 (or certain regions in the channel 170)significantly increases and/or decreases. In some embodiments, thepopulation surge metrics 511 can include data that identifies densitiesof individuals throughout a channel 170. For example, the populationsurge metrics 511 can indicate regions within the channel 170 wherethere is a high density of individuals and/or regions within the channelwhere there are lower densities of individuals. In some cases, thepopulation surge metrics 511 for a channel 511 can be generated, atleast in part, by monitoring locations (e.g., GPS coordinates) ofindividuals' computing devices 110 (e.g., smart phones or mobiledevices), determining the number of computing devices 110 that arelocated within the channel 170 (or region within the channel), andcomparing that number to a value indicating an average or baselinepopulation for the channel 170 (or region within the channel).

Each node 165 also can be configured to predict future channelconditions in which the population in the channel 170 will vary from theaverage or baseline population. These predictions can be generatedbased, at least in part, on an analysis of the channel events 175. Forexample, the event data 506 included in the channel events 175 can beutilized to identify times and locations in which a population surge islikely to occur in the channel 170 (or regions within the channel 170)as the result of an upcoming event (e.g., such as a concert).Additionally, or alternatively, the location data 501 included in thechannel events 175 can be used to predict upcoming expansions orretractions of populations within a channel 170 (or regions within achannel 170) based on an analysis of individuals' current locationsand/or based on historical movement patterns. Additionally, oralternatively, the weather data 505 included in the channel events 175also may be utilized to predict the population surge metrics 511 basedon expected weather conditions for future time periods.

The movement tracking metrics 512 generated by each node 165 can includevarious metrics indicating and/or predicting the locations or movementsof individuals within the channel 170 associated with the node 165. Themovement tracking metrics 512 can include data indicating whereindividuals have moved within the channel, and locations where thoseindividuals originated. The movement tracking metrics 512 also includedata that indicates movement or migration patterns of individuals (bothintra-channel movement patterns and inter-channel movement patterns).For example, based on an analysis of historical location data 501, themovement tracking metrics 512 may indicate historical movement patternsof individuals within the channel 170.

The movement tracking metrics 512 also can include data that predictsthe movements of individuals throughout a channel 170 in a future timeperiod. For example, the movement tracking metrics 512 can predictregions within a channel where individuals are likely like to migrate.The location tracking metrics 512 can be generated based, at least inpart, on the channel events 175 (e.g., such as the location data 501indicating current and historical locations of individuals, and eventdata 506 indicating current or upcoming events within the channel 170).

The demand metrics 513 generated by each node 165 can include variousmetrics indicating and/or predicting supply and/or demand within thechannel 170 for various inventory items (e.g., products and/or services)in one more verticals (e.g., riding sharing, hotel accommodations,parking garages, restaurants, bars, retail, etc.). The current or futuresupply and/or demand for inventory may be determined based, at least inpart, on an analysis of the population surge metrics 511 (e.g., whichcan indicate or predict the densities of individuals within the channel170 or regions within the channel 170). Additionally, or alternatively,the supply and/or demand for inventory also may be based on an analysisof channel events 175 received by the node 165, such as channel eventsthat include transaction data 502 (e.g., which may indicate recent orhistorical purchases made within the channel 170), weather data 505(e.g., which may indicate weather conditions affecting the demand forinventory), merchant data 504 (e.g., which may indicate the supply ofinventory in the channel 170), and/or other channel events 175.

The population surge metrics 511, movement tracking metrics 512, anddemand metrics 513 are provided as examples of channel analysis data 180that can be generated by a node 165. However, it should be recognizedthat the channel analysis data 180 can include many other types ofmetrics or analytics relevant to the conditions of the channels 170.

Returning to FIGS. 1A and 1B, the one or more computing devices 110 canenable individuals to access the analytics platform 150 over the network105 (e.g., over the Internet via a web browser application). Forexample, after a user account is established with the analytics platform150, a user may utilize the analytics platform 150 to access to channelanalysis data 180 generated by the analytics platform 180. In someembodiments, a user may be provided with access to the channel analysisdata 180 generated across all of the channels 170. In other embodiments,a user may designate particular channels 170 of interest and receivechannel analysis data 180 from the designated channels 170. In someembodiments, a user also may designate particular verticals orindustries of interest, and receive channel analysis data 180 pertainingspecifically to those verticals or industries.

The analytics platform 150 may generate various graphical userinterfaces (GUIs) that display the channel analysis data 180 and/orother associated data (e.g., channel event information, user accountprofiles, etc.), and these interfaces can be accessed via the useraccounts. The interfaces provided by the analytics platform 150 also caninclude selectable options for configuring one or more deploymentfunctions 190. The deployment functions 190 can permit users to leveragethe channel analysis data 180 (and other data generated by the analyticsplatform 150) for various purposes.

One exemplary deployment function 190 can include a notificationfunction 191. The notification function 191 enables users to configurethe transmission of notifications in various scenarios. Thenotifications can be transmitted in various ways (e.g., via e-mail,cellular text messages, inbox messages on user accounts, data presentedon GUIs, etc.) and the notifications can be sent to various devices(e.g., client systems 140, mobile or computing devices operated byindividuals located in a channel 170, etc.).

In some cases, the notification function 191 can be configured toperiodically transmit notifications that include channel analysis data180, including both real-time and predictive channel analysis data 180.In this scenario, the notifications may provide daily, weekly, monthlyand/or annual summaries of channel analysis data 160 relevant tospecific users. The notification function 191 also can be configured tosend notifications in response to trigger events that are defined orconfigured by users. In this scenario, users may specify or definetriggering criteria, and the analytics platform 150 may automaticallysend notifications to computing devices 110 and/or client systems 140when specific trigger events are detected or predicted by the analyticsplatform 150 (e.g., in response to detecting or predicting a surge inpopulation within the channel and/or a surge in demand for inventory).The notification function 191 also can be configured to sendnotifications to individuals (e.g., their mobile devices) located withina channel 170. In this scenario, the notifications can includeinformation about merchants located in a channel 170 and, in some cases,can include offers and discounts for various products and services.

Another exemplary deployment function 190 can include an interfacingfunction 192. The interfacing function 192 can permit a user tointerface the analytics platform 150 with various external applicationsand/or systems, thereby enabling those applications and/or systems toreceive and utilize the channel analysis data 180 generated by theanalytics platform 150.

In some embodiments, one or more of applications running on, or operatedby, the client systems 140 can be directly interfaced with the analyticsplatform 150 (e.g., via an application programming interface or APIprovided by the analytics platform 150). In some exemplary scenarios, anclient system 140 may execute or provide a ride hailing application, alodging booking application (e.g., hotel booking application), a diningreservation application (e.g., an application for scheduling diningreservations), a ticket booking application (e.g., for purchasingtickets to concerts, sporting games, and/or other events), a pricingapplication (e.g., an program that computes or determines prices forproducts and/or services), a staffing application (e.g., a program thatschedules employees), and/or an inventory management application (e.g.,a program that allocates inventory among different channels 170 orlocations, places orders for new inventory, etc.). The interfacingfunction 192 can connect the analytics system 150 to these applications(and other types of applications), thereby enabling the applications todirectly receive the channel analysis data 180 and utilize the channelanalysis data 180 to automate control of one or more functions (e.g.,such as determining or adjusting pricing information, adjustinginventory allocations, initiating purchases of additional inventory,adjusting staffing at locations, etc.).

In one example, the deployment functions 190 can be utilized toimplement surge pricing functions, which change or adjust the prices ofproducts and/or services based on a supply or demand for those productsand/or services. For example, the population surge metrics 511 and/ordemand metrics 511 included in the channel analysis data 180 for achannel 170 can be used to dynamically adjust the pricing for hotelrooms, ride hailing services, parking garage spaces, and/or other typesof inventory based on the demand for the inventory within the channel170. Similarly, the movement tracking metrics 512 can be utilized todetect particular regions within a channel 170 where demand is likely tobe higher and to adjust prices accordingly. These surge pricingfunctions can be automated by interfacing client systems 140 with theanalytics platform 150 (e.g., via the interfacing function 192) and/orthey can be perform manually based on a review of the channel analysisinformation 180.

In another example, the deployment functions 190 can be utilized byclient systems 140 to reallocate inventory or resources based on thechannel analysis data 180. For example, a client system 140 thatprovides ride-sharing services can reallocate drivers to regions orareas where demand is higher and/or expected to be higher. Along similarlines, a client system 140 that is affiliated with a restaurant within achannel 170 can place orders for additional inventory and/or adjustingstaffing in scenarios where demand is higher and/or expected to behigher. The channel analysis data and deployment functions 190 can beutilized in many others ways to enhance operations of merchants withinthe channels 170.

FIG. 2A illustrates an exemplary decentralized computing network 160comprising a plurality of nodes 165 arranged in a geographical hierarchy210. In some embodiments, the geographical hierarchy 210 comprises aplurality of layers (such as exemplary layers A, B, C, D . . . N), andeach of the layers can include one or more nodes 165. In general, thenodes 165 located in the upper layers of the geographical hierarchy 210encompass broader channels 170 or geographic areas than those includedin lower layers.

To illustrate by way of example, Layer A may include a global node 165that corresponds to a global channel (e.g., channel 170A), whichencompasses all geographic regions that are analyzed by the analyticsplatform 150. Layer B can include a plurality of nodes 165 thatcorrespond to country-level channels (e.g., such as channel 170B), eachof which encompasses a geographic region associated with a particularcountry. Layer C can include a plurality of nodes 165 that correspond tostate-level channels (e.g., such as channel 170C), each of whichencompasses a geographic region associated with a particular state,province, district or similar type of region within a country. Layer Dcan include a plurality of nodes that correspond to city-level channels(e.g., such as channel 170D), each of which encompasses a geographicregion associated with a particular city, town, village, or the like.Layer E can include a plurality of nodes that correspond to even moregranular channels (e.g., such as channel 170E), each of whichencompasses a geographic region (e.g., a neighborhood or area) within aparticular city, town, village, or the like.

Parent-child relationships 220 can be established among related nodes365 in the geographical hierarchy 210. A child node can represent achannel 170 (or geographic region) that is a subset of a channel 170associated with a parent node. Conversely, a parent node can represent achannel 170 (or geographic region) that is a superset of one or morechannels 170 associated with child nodes. Thus, each node may beconnected to parent node in an upper layer if that node is a subset ofthe node in the upper layer, and each node can be connected to one ormore child nodes in a lower layer if that node is a superset of thenodes in the lower layer.

For example, a first node in Layer C can be associated with a channelcovering the state of New York. Therefore, this node can be connected toa parent node in Layer B that corresponds to a channel covering theUnited States, and can be connected to a plurality of child nodes inLayer D corresponding to cities in the state of New York (e.g., New YorkCity, Buffalo, Rochester, etc.). Similarly, a second node in Layer C maycorrespond to a channel covering the province of British Columbia. Thisnode can be connected to a parent node in Layer B that corresponds to achannel encompassing the country of Canada, and can be connected aplurality of child nodes in Layer D corresponding to cities in theprovince of British Columbia. Along similar lines, the nodes in Layer Ecan correspond to specific regions (e.g., blocks, neighborhoods,boroughs, etc.) within particular cities, and can be connected tocorresponding nodes in Layer D.

A node communication protocol 167 (FIG. 1B) can enable communicationamong the nodes 165 in the decentralized computing network 160 based onthe parent-child relationships 220 among the nodes. For example, a givennode can exchange information with one or more parent nodes and/or oneor more child nodes based on geographical associations or relationships.The exchange of information among the nodes can be used, at least inpart, to generate the channel analysis data 180 across the nodes 165 ina manner that avoids redundancies and reduces storage and processingrequirements.

To illustrate by way of example, consider a scenario in which a node 165in Layer C (e.g., corresponding to a channel 170 for the State of NewYork) is computing demand metrics 513 that indicate or predict thedemand for one or more products or services in the channel 170. In thisscenario, the node communication protocol 167 enables the node in LayerC to communicate with and receive demand metrics 513 generated by thenodes in Layer D (e.g., that can correspond to channels for citieswithin the State of New York). The node in Layer C can compute thedemand metrics 513 for its channel 170, at least in part, using thedemand metrics received from the nodes in Layer D. Along similar lines,the nodes in Layer D can compute demand metrics 513 for correspondingchannels 170 using the demand metrics 513 that were computed bycorresponding child nodes in Layer E.

In this manner, the nodes 165 are able to leverage the data generated bythe related nodes and avoid processing the data associated with thosenodes, thereby avoiding redundancies in computations. The nodecommunication protocol 167 can enable the nodes to leverage theparent-child relationships 220 to compute other metrics included withthe channel analysis data 180 (e.g., such as population surge metrics511, movement tracking metrics 512, and/or other metrics describedherein).

Additionally, in some embodiments, the node communication protocol 167can include or utilize a blockchain protocol to record and trackcommunications among the nodes 165 of the decentralized computingnetwork 160. For example, each of the nodes 165 can store and maintain adistributed ledger or database that records the data exchanged among thenodes 165 (e.g., which may include channel analysis information 180and/or channel events 175). The distributed ledger or database can beupdated with a new block or entry each time data from one node 165 ispassed to another node 165, and the block or entry can be appended withvarious attributes (e.g., data indicating the node receivinginformation, the node sending information, the information that wasexchanged, the date/time of exchange, etc.). In some scenarios, aconsensus mechanism or protocol may be utilized to approve the additionof new blocks to the distributed ledger or database and/or to ensure asingle, valid copy of the distributed ledger or database is shared byall of the nodes 165.

For ease of understanding, the examples discussed above (and in otherportions of this disclosure) describe geographical hierarchies 210 orchannels 170 that are based on political boundaries, such as thosecorresponding to countries, states, cities, etc. However, it should berecognized that the geographical hierarchies 210 and channels 170 can bedefined in many other ways that are not associated with politicalboundaries. For example, the channels 170 associated with the nodes 165can correspond to arbitrarily selected geographic regions that are nottied to political boundaries and/or can correspond to geographic regionsthat are selected based on longitude and latitude coordinates.

FIG. 2B illustrates an exemplary cloud-based computing environment 200that is configured to host and/or implement the decentralized computingnetwork 160. In certain embodiments, the cloud-based computingenvironment 200 can include a plurality of servers (e.g., such asservers 120) that provide, inter alia, a shared pool of processing,memory, and/or storage resources.

In this exemplary environment, each of the nodes 165 included in thedecentralized computing network 160 can be executed on a separatevirtual machine (VM) 230 (e.g., such as virtual machines 1, 2, . . . .N). Each of the virtual machines 230 can utilize dedicated memoryresources 201 and dedicated processing resources 202 to execute thefunctionalities associated with a corresponding node 165. The dedicatedmemory resources 201 can include one or more computer storage devices(e.g., such as computer storage devices 101) selected from a shared poolof server resources. The dedicated processing resources 202 can includeone or more processing devices (e.g., such as processing devices 102)selected from a shared pool of server resources.

In certain embodiments, each of the virtual machines 230 can represent aprocess virtual machine (also referred to as an application virtualmachine) that executes the functions associated with a node 165,including receiving channel events 175, executing the channel analysisfunction 166, and generating channel analysis information 180. Asexplained above, a node communication protocol 167 enables the nodes 165(and corresponding virtual machines 230) to communicate with each otherfor generating channel analysis data 180.

The node manager 250 can operate as a virtual machine manager (VMM)and/or hypervisor that manages the creation and execution of the virtualmachines 230 in the cloud-based computing environment 200. Additionally,or alternatively, the node manager 250 can perform functions associatedwith allocating or assigning the nodes 165 to the virtual machines 230.

The node manager 250 also can perform operations associated withallocating resources (e.g., dedicated memory resources 201, dedicatedprocessing resources 202, storage resources, etc.), to each of thevirtual machines 230 and/or to each of the nodes 165 associated with thevirtual machines 230. In some instances, the node manager 250 canmonitor the workloads associated with each of the nodes 165 anddynamically adjust or allocate memory resources 201 and processingresources 202 to the nodes 165 (or virtual machines 230 executing thenodes 165) based on the workloads of the nodes 165. For example, inscenarios in which a node 165 has a large workload (e.g., because it isrequired to process data for large populations of individuals), the nodemanager 250 can allocate additional memory resources 201 and processingresources 202 to the node 165

In certain embodiments, the node manager 250 also can perform operationsthat allocate nodes 165 to the virtual machines 230 in the cloud-basedcomputing environment 200. In some instances, the node manager 250 mayallocate one node to each of the virtual machines 230. In otherinstances, the node manager 250 may allocate a plurality of nodes tosome or all of the virtual machines 230.

Additionally, the node manager 250 can perform operations that associatenodes 165 with the channels 170. In some embodiments, the node manager250 can associate one node 170 with each of the channels 170 and, inother instances, the node manager 250 can allocate a plurality of nodes165 to each of the channels 170 or some of the channels 170. Forexample, in scenarios where the workload of node 165 is high, the nodemanager 250 can allocate additional nodes 165 to help process theworkload for a corresponding channel 170.

The decentralized computing network 160 can be implemented in otherenvironments and configurations. For example, the decentralizedcomputing network 160 can be implemented in alternative cloud-basedcomputing environments and configurations. Additionally, thedecentralized computing network 160 can be implemented without the usageof any cloud-based computing environment. For example, in someembodiments, a plurality of hardware servers, each of which is dedicatedto execute the functionality of a corresponding node 165, can beutilized to implement the decentralized computing network 160. Thedecentralized computing network 160 can be implemented in other ways aswell.

FIG. 3 is an illustration demonstrating channel analysis data 180 beinggenerated across various multiple verticals 310 according to certainembodiments. The decentralized computing network 160 includes aplurality of nodes 165, each of which are dedicated to channel 170. Eachnode 165 receives various channel events 175 corresponding to itsdedicated channel 170. Each node 165 can process and analyze the channelevents 175 to generate channel analysis data 180, which can includereal-time metrics pertaining to current channel conditions and/orpredictive metrics pertaining to future channel conditions.

This real-time data and/or predictive data can be generated for aplurality of verticals 310 (e.g., business sectors or industries),including those related to transportation, lodging, restaurants, ticketbooking, parking services, etc. In certain embodiments, the channelanalysis data 180 can be customized specifically to each of theverticals 310. In some scenarios, the channel analysis data 180 canseparately compute demand metrics 513 for each vertical 310 and/or forinventory items associated with each vertical 310. For example, demandmetrics 513 can be separately computed for inventory items correspondingto room availabilities for a hotel or lodging vertical, vehicleavailabilities for a ride hailing or transportation vertical, parkingspace availabilities for a garage or parking vertical, reservationavailabilities for a restaurant or dining vertical, etc.

As mentioned above, the analytics platform 150 can generatenotifications and/or GUIs that display or include some or all of thechannel analysis data 180. In some cases, these GUIs and notificationscan be customized or tailored for to present metrics or data forspecific verticals 310. Similarly, any data provided directly to clientsystems 140 with the analytics platform 150 can be customized ortailored for specific verticals 310 and/or specific client systems 140.

FIG. 4 is an illustration demonstrating aspects of a real-time engine410 and a predictive engine 420 according to certain embodiments. Incertain embodiments, the real-time engine 410 and the predictive engine420 can represent subroutines that are executed by a channel analysisfunction 166 for a node 165 to generate channel analysis data 180 for achannel 170 associated with the node 165.

The real-time engine 410 can be configured to generate channel analysisdata 180 pertaining to the current or real-time conditions of thechannel 170, and the predictive engine 420 can be configured to generatechannel analysis data 180 pertaining to predicted conditions of thechannel 170 in one or more future time periods. For example, thereal-time engine 410 can generate population surge metrics 511, movementtracking metrics 512, and demand metrics 513 indicating the real-timeconditions of the channel 170, while the predictive engine 420 cangenerate population surge metrics 511, movement tracking metrics 512,and demand metrics 513 predicting conditions of the channel 170 in oneor more future time periods. As explained above, the real-time engine410 and predictive engine 420 can utilize various channel events 175(e.g., indicating spending habits, schedule events, weather conditions,historical movements of individuals, population density, etc.) todetermine the current and future conditions of the channels.

FIG. 6 is a block diagram illustrating exemplary features, components,and/or functions of a client system 140 according to certainembodiments. The client system 140 can include one or computing devices110 and/or one or more servers 120, each of which includes one or morecomputer storage devices 101 and one or more processor devices 102. Theclient system 140 can host and execute one or more client applications610.

Exemplary client applications 610 provided by a client system 140 caninclude one or more of the following: 1) a ride hailing application 611(e.g., an application that connects passengers with drivers to schedulerides); 2) an accommodation application 612 (e.g., an application thatpermits guests to schedule rooms or lodging); 3) a travel application613 (e.g., an application that permits individuals to book scheduletransportation with airlines, trains, buses, boats, etc.); and 4) areservation application 614 (e.g., an application that permitsindividuals to schedule reservations or tickets for restaurants,concerts, events, bars, parking spaces, and/or other venues). Othertypes of client applications 610 also may be hosted and executed by theclient systems 140. In certain embodiments, the client applications 610can represent web-based applications that are accessible via a webbrowser and/or local applications (e.g., mobile apps) that is installedon devices (e.g., mobile devices or smart phones) operated by end-users.

The channel analysis data 180 (e.g., the population surge metrics 511,movement tracking metrics 512, demand metrics 513, and/or other datadescribed herein) can be utilized to enhance various functionalities ofthe client applications 610. In some scenarios, the channel analysisdata 180 can be utilized to enhance or implement one or more demandadjustment functions 650, such as a pricing function 620 and/or aninventory management function 630, for each of the client applications160. The demand adjustment functions 650 can be configured to adjustpricing and/or inventory information for inventory items based on actualor predicted supply metrics and/or demand metrics for the inventoryitems. The pricing function 620 and/or inventory management function 630can be included with the functionality of each of the clientapplications 610, or can be included in separate applications thatcommunicate with the client applications 610.

The pricing function 620 can utilize the channel analysis data 180 todetermine pricing for one or more inventory items 635, which maygenerally include any type of product or service made available by aclient application. For example, depending on the functionality of agiven client application 160, the pricing function 620 can determinepricing for ride hailing services, taxi services, lodgingaccommodations, event tickets (e.g., for sporting events or concerts),airline tickets, train tickets, packing spaces, etc.

In some scenarios, the channel analysis data 180 can be utilized toimplement a surge pricing function 625 for one or more of the clientapplications 610. A surge pricing function 625 generally represents afunction that adjusts the price of one or more inventory items 635 basedon the demand for the inventory items 635 (e.g., based on a comparisonof the supply and the demand for the inventory items 635). The demandmetrics 513 (and/or other channel analysis data 180) generated by theanalytics platform 150 may be utilized to dynamically adjust the pricingof one or more inventory items 635 offered by each of the clientapplications 610.

The inventory management function 630 can utilize the channel analysisdata 180 to manage or adjust inventory items 635 in various ways. Forexample, the inventory management function 630 can detect whenadditional inventory items 635 should be ordered to accommodate acurrent demand for inventory items 635 and/or a predicted future demandfor inventory 635. In some scenarios, the inventory management function630 also can be configured to automatically place an order foradditional inventory items 635 to accommodate a spike in a current orpredicted demand for the inventory items 635.

The inventory management function 630 also can reallocate inventoryitems 635 to accommodate varying supply and demand metrics acrossdifferent channels 170 and/or within a given channel 170. For example,in some scenarios, a merchant may have multiple business locations,including multiple locations within a given channel 170 and multiplelocations situated outside the channel 170. The inventory managementfunction 630 can utilize the channel analysis data 180 to dynamicallyreallocate inventory items 635 among the business locations in withinthe given channel 170 to accommodate the varying demands at thoselocations and/or to maximize sales of inventory items 635 across alllocations. Similarly, the inventory management function 630 can utilizethe channel analysis data 180 to dynamically reallocate inventory items635 from a location in one channel 180 to one or more separate channelsin order to accommodate the varying demands in each channel 170 and/orto maximize sales of inventory items 635 across all channels 170.

In certain embodiments, the client applications 610 can additionally, oralternatively, be stored on and executed by the analytics platform 150.Similarly, the demand adjustment functions 650 (e.g., including thepricing function 620, the surge pricing function 625, and/or theinventory management function 630) can be stored on and executed by theanalytics platform 150.

For example, in certain embodiments, the decentralized computing network160 can be utilized to execute the demand adjustment functions 650. Inaddition to generating the channel analysis data 180, each node 165 ofthe decentralized computing network 160 can execute one or more demandadjustment functions 650 for a channel 170 corresponding to the node165. In one example, each node 165 can execute a surge pricing function225 that utilizes the channel analysis data (e.g., the population surgemetrics 511 and/or demand metrics 513) to dynamically adjust the pricingof one or more inventory items 635. In another example, each node 165can execute an inventory management function 630 that dynamicallyallocates or reallocates inventory items 635 within a channel 170 and/oramong a plurality of channels 170.

FIGS. 2C and 2D illustrate examples of a decentralized computing network160 that is configured to execute demand adjustment functions 650.

In FIG. 2C, four nodes 165 are identified by cross-hashing to indicatethat the nodes 165 have detected channel conditions indicating theoccurrence of a demand surge 290 for one or more inventory items 635 (ora demand surge 290 in one or more verticals 310) in their correspondingchannels 170 (channels 170C, 170F, 170G, and 170H). The demand surge 290can indicate an upward fluctuation in the demand for the one or moreinventory items 635 (e.g., in scenarios where there is a high demand ordemand is increasing) and/or a downward fluctuation in the demand forthe one or more inventory items 635 (e.g., in scenarios where there islow demand or demand is decreasing). The other nodes 165 (withoutcross-hashing) correspond to nodes that have not detected a demand surge290 in their corresponding channels 170.

In some cases, each node 165 may detect the occurrence of demand surge290 by comparing a first value indicating an actual or predicted demandfor one or more inventory items 635 (which may be included in the demandmetrics 513 computed by the node 165) with a second value indicating abaseline or standard demand for a channel 170. Additionally, oralternatively, each node 165 can detect the occurrence of demand surge290 by comparing the first value with a threshold value (e.g., such thata demand surge 290 is detected when the first value exceeds or is belowthe threshold value). The demand surge 290 can be detected in other waysas well.

In FIG. 2D, each of the nodes 165 included in the decentralizedcomputing network 160 have computed a multiplier (or other value) to beutilized in connection with one or more demand adjustment functions 650.Nodes 165 that have not detected a demand surge 290 indicating anupwardly trending demand in corresponding channels 170 are shown with amultiplier of one (×1). The four nodes identified by cross-hashing inFIG. 2C (i.e., the nodes 165 that have detected an upwardly trendingdemand surge 290 in their corresponding channels 170) have computedgreater multipliers (×1.4, ×2.0, ×2.4, and 1.2), each of whichcorresponds to the severity or level of the demand surge 290 in acorresponding channel 170.

The multipliers computed by the nodes can be utilized by dynamicallymodify or update one or more demand adjustment functions 650. Forexample, consider a scenario in which the nodes 165 are executing asurge pricing function 625 that dynamically adjusts prices for one ormore inventory items 635 in channels 170 corresponding to the nodes 165.In this scenario, the multipliers can be utilized to increase the pricesfor the one or more inventory items 635. Each node 165 may determine anadjusted price for the one or more inventory items 635 by multiplying abaseline price with the multiplier that was computed by the node 165. Insome instances, the adjusted price, which reflects an adjustment basedon the demand surge, can be utilized by a client application 160 toprice the one or more inventory items 635 (e.g., to adjust prices forride hailing services, hotel rooms, event tickets, etc.).

The multipliers computed by the nodes 165 also can be utilized by aninventory management function 630 to dynamically adjust allocations ofinventory items 635. For example, consider a scenario in which a ridehailing service is experiencing a population surge or demand surge in afirst set of channels 170, while population or demand conditions in asecond set of channels 170 remains normal or constant. In this scenario,the ride hailing service may wish to reallocate inventory items 635(e.g., drivers) from the channels 170 with lower demand to the channels170 that are experiencing greater demand. The multipliers computed bythe nodes 165 can be utilized to determine the amount of inventory thatshould be transferred to the channels 170 with greater demand.

In some embodiments, each of the nodes 165 also be configured to detector determine a supply surge (rather than a demand surge 290, or inaddition to calculating the demand surge 290). The supply surge canindicate an upward fluctuation in the supply for the one or moreinventory items 635 (e.g., in scenarios where there is a high supply orthe supply is increasing) and/or a downward fluctuation in the supplyfor the one or more inventory items 635 (e.g., in scenarios where thereis low supply or supply is decreasing). This supply surge can beutilized in a similar manner to the demand surge 290, such as toimplement pricing adjustment functions and/or inventory managementfunctions. For example, a high supply surge may result in loweringprices or inventory levels, while a low supply may result in raisingprices or inventory levels.

In some instances, the supply surge or supply-related information can beutilized in computing the demand surge 290. For example, the detectionof a demand surge 290 can involve consideration of both the supply anddemand for one or more inventory items 635, and the one or more demandadjustment functions 610 can adjust pricing information and/or inventoryallocations based on a joint consideration of the supply and demandinformation for the one or more inventory items 635.

FIG. 7 illustrates a flow chart for an exemplary method 700 according tocertain embodiments. Method 700 is merely exemplary and is not limitedto the embodiments presented herein. Method 700 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, the steps of method 700 can be performed inthe order presented. In other embodiments, the steps of method 700 canbe performed in any suitable order. In still other embodiments, one ormore of the steps of method 700 can be combined or skipped. In manyembodiments, system 100, analytics platform 150, and/or decentralizedcomputing network 160 can be configured to perform method 700 and/or oneor more of the steps of method 700. In these or other embodiments, oneor more of the steps of method 700 can be implemented as one or morecomputer instructions configured to run at one or more processingdevices 102 and configured to be stored at one or more non-transitorycomputer storage devices 101. Such non-transitory memory storage devices101 can be part of a computer system such as system 100, analyticsplatform 150, and/or decentralized computing network 160.

In step 710, a decentralized computing architecture or network 160comprising a plurality of nodes 165 is provided. In certain embodiments,the nodes 165 included in the decentralized computing architecture 160can be arranged in a geographical hierarchy 210 that links the nodes 165using parent-child relationships 220 based on geographical associations.

In step 720, each node 165 of the decentralized computing architectureor network 160 is associated with a separate channel 170 correspondingto a geographic region. For example, each node 175 in the decentralizedcomputing architecture 160 can be dedicated to a particular channel 170such that the node 175 receives and processes data exclusively and/orindependently for the channel 170.

In step 730, each node 165 of the decentralized computing architectureor network 160 receives and analyzes channel events 175 for the channel170 associated with node 165. For example, each node 165 can receivevarious types of channels events 175 relating to conditions of anassociated channel 170. Exemplary channel events 175 can includelocation data 501, transaction data 502, user demographic data 503,merchant data 504, weather data 505, and/or event data 505.

In step 740, each node 165 generates channel analysis data 180corresponding to the channel 170 associated with the node 165 based, atleast in part, on an analysis of the channel events 175 corresponding tothe channel 170 associated with the node 165. The channel analysis data180 can be include various metrics including, but not limited to,population surge metrics 511, movement tracking metrics 512, and/ordemand metrics 513. The channel analysis data 180 can include bothreal-time data that indicates current conditions of the channel 170and/or predictive data that predicts future conditions of the channel.

In some embodiments, the decentralized computing network 160 can utilizethe channel analysis data 180 to execute one or more demand adjustmentfunctions, such as a pricing function 620, surge pricing function 625,and/or inventory management function 630. In certain instances, each ofthe nodes 165 in the decentralized computing network 160 can analyze thechannel analysis data 180 (e.g., the demand metrics 513) to determine ifa demand surge 290 exists for an inventory item 635 in a correspondingchannel 170. In response to detecting a demand surge 290 in a channel170, the node 170 associated with the channel 170 can determine amultiplier (or other value) that is used to determine and/or adjust theprice of inventory item 635 or used to adjust allocations of theinventory items 635.

In step 750, one or more deployment functions 190 can be executed toenable the channel analysis data 180 to be accessed and/or utilized byone or more client systems 140. The deployment functions 190 can enablethe client systems 140 to utilize the channel analysis data 180 toenhance the functionalities of one or more client applications 610. Oneexemplary deployment function 190 can include a notification function191 that transmits notifications to the one or more client systems 140and/or displays the notifications on one of more GUIs that areaccessible via an analytics platform 150. Another exemplary deployment190 can include an interfacing function 192 that interfaces theanalytics platform 150 with various the client applications 610 and/orclient systems 140, thereby enabling those external applications and/orsystems to directly access the channel analysis data 180. In someinstances, a client application 610 that is interfaced with theanalytics platform 150 can utilize the channel analysis data to executea pricing function 620, surge price function 625, and/or an inventorymanagement function 630. As evidenced by the disclosure herein, theinventive techniques set forth in this disclosure are rooted in computertechnologies that overcome existing problems in known networking anddata processing systems, including problems dealing with utilizing acentralized network architecture to process data over multiple channelsor geographic regions. The techniques described in this disclosureprovide a technical solution (e.g., that utilizes improved decentralizedcomputing networks) for overcoming the limitations associated with knowntechniques. This technology-based solution marks an improvement overexisting capabilities and functionalities related to processing dataacross multiple channels or geographic areas.

In certain embodiments, the techniques described herein can be utilizedcontinuously at a scale that cannot be reasonably performed using manualtechniques or the human mind. For example, in many embodiments,real-time information from large numbers of channels or geographic areascan be simultaneously processed and analyzed to provide real-timeupdates to client systems. This simultaneous processing of real-timedata cannot be performed by a human mind.

Additionally, in certain embodiments, the techniques described hereinsolve a technical problem that arises only within the realm of computernetworks, as decentralized computer networks or architectures do notexist outside the realm of computer networks.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer-readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be a magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable storage medium, such as a semiconductor or solid-statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk, etc.

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories that provide temporary storage of at leastsome program code to reduce the number of times code is retrieved frombulk storage during execution. Input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) may becoupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems, and Ethernet cards are just a few of thecurrently available types of network adapters.

It should be recognized that any features and/or functionalitiesdescribed for an embodiment in this application can be incorporated intoany other embodiment mentioned in this disclosure. Moreover, theembodiments described in this disclosure can be combined in variousways. Additionally, while the description herein may describe certainembodiments, features, or components as being implemented in software orhardware, it should be recognized that any embodiment, feature, orcomponent that is described in the present application may beimplemented in hardware, software, or a combination of the two.

While various novel features of the invention have been shown,described, and pointed out as applied to particular embodiments thereof,it should be understood that various omissions and substitutions, andchanges in the form and details of the systems and methods described andillustrated, may be made by those skilled in the art without departingfrom the spirit of the invention. Amongst other things, the steps in themethods may be carried out in different orders in many cases where suchmay be appropriate. Those skilled in the art will recognize, based onthe above disclosure and an understanding of the teachings of theinvention, that the particular hardware and devices that are part of thesystem described herein, and the general functionality provided by andincorporated therein, may vary in different embodiments of theinvention. Accordingly, the description of system components are forillustrative purposes to facilitate a full and complete understandingand appreciation of the various aspects and functionality of particularembodiments of the invention as realized in system and methodembodiments thereof. Those skilled in the art will appreciate that theinvention can be practiced in other than the described embodiments,which are presented for purposes of illustration and not limitation.Variations, modifications, and other implementations of what isdescribed herein may occur to those of ordinary skill in the art withoutdeparting from the spirit and scope of the present invention and itsclaims.

The invention claimed is:
 1. A computerized method for processing dataacross multiple channels, comprising: providing a decentralizedcomputing network comprising a plurality of nodes, wherein the pluralityof nodes included in the decentralized computing network are arranged ina geographical hierarchy that defines parent-child relationships basedon geographic associations; associating each node of the decentralizedcomputing network with a separate channel corresponding to a geographicregion; configuring each node of the decentralized computing network toreceive and analyze channel events for the channel associated with thenode; generating, by each node of the decentralized computing network,channel analysis data corresponding to the channel associated with thenode, wherein the channel analysis data for the channel is generatedbased, at least in part, on an analysis of the channel eventscorresponding to the channel associated with the node; detecting, by atleast one of the plurality of nodes, a demand surge in one or morechannels based on the channel analysis data; and in response todetecting the demand surge, executing a demand adjustment function thatadjusts the price of one or more inventory items based, at least inpart, on a demand for the one or more inventory items in the one or morechannels or adjust allocations of the one or more inventory items based,at least in part, on the demand for the one or more inventory items inthe one or more channels.
 2. The computerized method of claim 1, whereina node communication protocol enables exchange of data among theplurality of nodes based, at least in part, on the parent-childrelationships included in the geographical hierarchy.
 3. Thecomputerized method of claim 2, wherein: the node communication protocolenables a parent node to obtain the channel analysis data generated byone or more child nodes; and the channel analysis data generated by theone or more child nodes is utilized by the parent node to generate thechannel analysis data for the channel associated with the parent node.4. The computerized method of claim 2, wherein the node communicationprotocol utilizes a blockchain protocol to facilitate the exchange ofdata among the plurality of nodes.
 5. The computerized method of claim1, wherein the method further comprises: executing one or moredeployment functions that enable the channel analysis data to beaccessed by one or more client systems, wherein the one or moredeployment functions facilitate interfacing with the one or more clientsystems and enable the channel analysis data to be provided directly tothe one or more client systems.
 6. The computerized method of claim 1,wherein the method further comprises: executing one or more deploymentfunctions that enable transmission of notifications to the one or moreclient systems, wherein the notifications include at least a portion ofthe channel analysis data.
 7. The computerized method of claim 1,wherein the channel analysis data generated by each node comprisespredictive metrics pertaining to future conditions for the channelassociated with the node.
 8. The computerized method of claim 1, whereinthe channel analysis data generated by each node comprises metricspertaining to actual or current conditions for the channel associatedwith the node.
 9. The computerized method of claim 1, wherein thechannel analysis data generated by each node comprises: populationmetrics predicting or indicating population fluctuations in the channelassociated with the node; movement tracking metrics predicting orindicating movements of individuals within the channel associated withthe node; and demand metrics predicting or indicating a demand for oneor more inventory items in the channel associated with the node.
 10. Thecomputerized method of claim 1, wherein the demand adjustment functionincludes at least one of: a surge pricing function that is configured todynamically adjust pricing for one or more inventory items based, atleast in part, on a supply or demand for the inventory items; or aninventory management function that adjusts allocations of the inventoryitems based, at least in part, on a supply or demand for the inventoryitems.
 11. The computerized method of claim 1, wherein the demand surgeindicates an upward or downward fluctuation in the demand for the one ormore inventory items.
 12. A system for processing data across multiplechannels, comprising: a decentralized computing network comprising oneor more processing devices and one or more non-transitory computerstorage devices storing computing instructions configured to be executedon the one or more processors and cause the one or more processors toprocess data for a plurality of nodes wherein: the plurality of nodesincluded in the decentralized computing network are arranged in ageographical hierarchy that defines parent-child relationships based ongeographic associations; each node in the decentralized computingnetwork is dedicated to a separate channel corresponding to a geographicregion; each node in the decentralized computing network receiveschannel events corresponding to the channel associated with the node;each node in the decentralized computing network is configured togenerate channel analysis data based, at least in part, on an analysisof the channel events corresponding to the channel associated with thenode; based on the channel analysis data, each node in the decentralizedcomputing network is configured to determine whether a demand surge isoccurring in the channel associated with the node; and in response todetecting the demand surge, a demand adjustment function is executed toadjust the price of one or more inventory items based, at least in part,on a demand for the one or more inventory items in the channelassociated with the node or adjust allocations of the one or moreinventory items based, at least in part, on the demand for the one ormore inventory items.
 13. The system of claim 12, wherein a nodecommunication protocol enables exchange of data among the plurality ofnodes based, at least in part, on the parent-child relationshipsincluded in the geographical hierarchy.
 14. The system of claim 13,wherein: the node communication protocol enables a parent node to obtainthe channel analysis data generated by one or more child nodes; and thechannel analysis data generated by the one or more child nodes isutilized by the parent node to generate the channel analysis data forthe channel associated with the parent node.
 15. The system of claim 13,wherein the node communication protocol utilizes a blockchain protocolto facilitate the exchange of data among the plurality of nodes.
 16. Thesystem of claim 12, wherein one or more deployment functions allow forinterfacing with one or more client systems and enable the channelanalysis data to be provided directly to the one or more client systems.17. The system of claim 12, wherein one or more deployment functionsenable transmission of notifications to the one or more client systems,and the notifications include at least a portion of the channel analysisdata.
 18. The system of claim 12, wherein the channel analysis datagenerated by each node comprises predictive metrics pertaining to futureconditions for the channel associated with the node.
 19. The system ofclaim 12, wherein the channel analysis data generated by each nodecomprises metrics pertaining to actual or current conditions for thechannel associated with the node.
 20. The system of claim 12, whereinthe channel analysis data generated by each node comprises: populationmetrics predicting or indicating population fluctuations in the channelassociated with the node; movement tracking metrics predicting orindicating movements of individuals within the channel associated withthe node; and demand metrics predicting or indicating a demand for oneor more inventory items in the channel associated with the node.
 21. Thesystem of claim 12, wherein the demand adjustment functions comprises: asurge pricing function that is configured to dynamically adjust pricingfor one or more inventory items based, at least in part, on a supply ordemand for the inventory items; or an inventory management function thatadjusts allocations of the inventory items based, at least in part, on asupply or demand for the inventory items.
 22. The system of claim 12,wherein the demand surge indicates an upward or downward fluctuation inthe demand for the one or more inventory items.
 23. A computerizedmethod for processing data across multiple channels, comprising:providing a decentralized computing network comprising a plurality ofnodes; associating each node of the decentralized computing network witha separate channel corresponding to a geographic region; configuringeach node of the decentralized computing network to receive and analyzechannel events for the channel associated with the node; generating, byeach node of the decentralized computing network, channel analysis datacorresponding to the channel associated with the node, wherein thechannel analysis data for the channel is generated based, at least inpart, on an analysis of the channel events corresponding to the channelassociated with the node, and the channel analysis data generated byeach node comprises: population metrics predicting or indicatingpopulation fluctuations in the channel associated with the node;movement tracking metrics predicting or indicating movements ofindividuals within the channel associated with the node; and demandmetrics predicting or indicating a demand for one or more inventoryitems in the channel associated with the node; detecting, by at leastone of the plurality of nodes, a demand surge in one or more channelsbased on the channel analysis data; and in response to detecting thedemand surge, executing a demand adjustment function that adjusts theprice of one or more inventory items based, at least in part, on thedemand for the one or more inventory items in the one or more channelsor adjust allocations of the one or more inventory items based, at leastin part, on the demand for the one or more inventory items in the one ormore channels.
 24. A system for processing data across multiplechannels, comprising: a decentralized computing network comprising oneor more processing devices and one or more non-transitory computerstorage devices storing computing instructions configured to be executedon the one or more processors and cause the one or more processors toprocess data for a plurality of nodes wherein: each node in thedecentralized computing network is dedicated to a separate channelcorresponding to a geographic region; each node in the decentralizedcomputing network receives channel events corresponding to the channelassociated with the node; each node in the decentralized computingnetwork is configured to generate channel analysis data based, at leastin part, on an analysis of the channel events corresponding to thechannel associated with the node, and the channel analysis datagenerated by each node comprises: population metrics predicting orindicating population fluctuations in the channel associated with thenode; movement tracking metrics predicting or indicating movements ofindividuals within the channel associated with the node; and demandmetrics predicting or indicating a demand for one or more inventoryitems in the channel associated with the node; based on the channelanalysis data, each node in the decentralized computing network isconfigured to determine whether a demand surge is occurring in thechannel associated with the node; and in response to detecting thedemand surge, a demand adjustment function is executed to adjust theprice of one or more inventory items based, at least in part, on thedemand for the one or more inventory items in the channel associatedwith the node or adjust allocations of the one or more inventory itemsbased, at least in part, on the demand for the one or more inventoryitems.